Section outline

  • Deploying ML Models on Robots

    Deploying machine learning (ML) models on actual robots brings together software intelligence and physical action. This section walks through the process of integrating trained models with robotic systems, covering hardware considerations, inference strategies, and real-time data processing.

    • 🧠 Why Deploy Models Locally?

      • Real-time inference: Robots need to make decisions instantly without relying on cloud latency.
      • Offline capability: Deploying models locally ensures operation even in environments without internet access.
      • Privacy and security: Sensitive data can remain on-device, reducing risks of transmission.

      🔧 Hardware Considerations

      When choosing a platform for ML deployment, consider the robot’s hardware capabilities. Popular choices include:

      • Raspberry Pi: Affordable and widely supported for running TensorFlow Lite models.
      • NVIDIA Jetson Nano: Offers GPU acceleration for more complex ML models.
      • ESP32 with Edge Impulse: Suitable for very lightweight ML applications like gesture recognition or vibration detection.

    • 📦 Converting Models to TFLite Format

      Before deployment, ML models (trained in TensorFlow or other frameworks) must be converted into the lightweight .tflite format:

      import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_saved_model('model_folder')
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
    f.write(tflite_model)

      This reduced-size format makes inference faster and suitable for devices with limited memory and processing power.

      🤖 Real-Time Inference on a Robot

      Once the .tflite model is loaded onto the robot, the following steps enable real-time predictions:

      1. Capture sensor or camera data continuously.
      2. Preprocess the input (resize, normalize, reshape).
      3. Feed it into the ML model and retrieve prediction.
      4. Trigger an action based on prediction results (e.g., move servo, sound buzzer).

      Example using TensorFlow Lite interpreter on a Raspberry Pi:

      import tflite_runtime.interpreter as tflite
interpreter = tflite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Set input tensor
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()

# Get output tensor
output_data = interpreter.get_tensor(output_details[0]['index'])

      ⚙️ Optimizations for Embedded Inference

      • Quantization: Reduces model size by converting weights from float32 to int8 without much loss in accuracy.
      • Edge TPU compilation: If using Google Coral, models must be compiled specifically for Edge TPU.
      • Memory management: Use efficient data structures and avoid redundant memory allocations during inference.

      📌 Deployment Checklist

      • Model is converted and tested in .tflite format.
      • Robot hardware supports required compute resources.
      • Input pipeline (camera/sensor) works in real-time.
      • Proper exception handling for inference failures or I/O lag.

      With your model running on your robot, you’ve now bridged the gap between intelligent predictions and physical responses. In the next section, you will apply this knowledge to a full-fledged object recognition and sorting project.